Interpretation of electrocardiography (ECG) by using polynomial function simulation
- 1. Interpretation of electrocardiography (ECG) by using polynomial function simulation Fikret Selim TACETTİN Yiğit TÜRK
- 2. Necessity References: https://world-heart-federation.org/news/deaths-from-cardiovascular-disease-surged-60-globally- over-the-last-30-years-report/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618868/ 12,1M 17,8M 20,5M 1990 2017 2021 #ofDeathsfromCVD(CardiovascularDisease) Worlwide • Heart Disease is leading cause for death • It is increasing year by year • Even in the Covid-19 period, heart disease kept its leading position • In fact, heart disease can be prevented with early detection • ECG (Electrocardiography) is a beneficial tool to detect heart disease. • Interpretation of ECG can be done via AI Heart Disease; 33% Cancer; 19% Chronic Respiratory Diseases; 7% Digestive Diseases; 4,5% Others; 37,5% Death Reasons in 2019
- 3. Objectives • Doctors are interpreting ECG (Electrocardiography) by evaluating characteristics of P-Q-R-S-T waves. • A machine learning model can be trained to accurately predict heart disease. Hypothesis • Develop a ML model with some unique parameters to predict rhythm related heart diseases more accurately • Our results should be better than previous studies. • Prepare a software for easy usage Goal
- 4. Introduction - What is ECG? • For a healthy person, electrical signals coming from 1 heartbeat can be seen in left figure. • The changes in P,Q,R,S,T peaks Height of peak Duration of peak are the alerts for different heart diseases 1
- 5. Introduction – New Approach Previous Studies for ECG interpretation • Neural Networks • Fourier Transformation • Gradient Boosting Tree • Genetic Algorithm • Polynomial Regression are used to predict heart disease from ECG 2 Our Study • Polynomial simulation (A method developed by ourselves to force the polynomial function passing from certain points) • Random Forest algorithms are used to predict heart disease from ECG 3 With our unique method ‘Polynomial Simulation’; the basic characteristics of ECG, like «height of R peak», «QRS interval» are reflected better than others
- 6. Introduction - DataSet • 12-lead ECGs of 10,646 patients • 500 Hz sampling rate • Each consists of 10-second • All diseases labeled by professional experts • Created under the auspices of Chapman University and Shaoxing People’s Hospital 4 SB; 3889 SR; 1826 AFIB; 1780 ST; 1568 SVT; 587 AF; 445 SA; 399 AT; 121 AVNRT; 16 AVRT; 8 SAAWR; 7 # of Patients in Dataset SB Sinus Bradycardia SR Sinus Rhythm AFIB Atrial Fibrillation ST Sinus Tachycardia AF Atrial Flutter SA Sinus Atrium SVT Supraventricular Tachycardia AT Atrial Tachycardia AVNRT Atrioventricular Node Reentrant Tachycardia AVRT Atrioventricular Reentrant Tachycardia SAAWR Sinus Atrium to Atrial Wandering Rhythm * SR (Sinus Rhythm) means healthy person
- 7. Introduction – How to detect disease from ECG? An AFIB example 2 example ECG; the disease and its characteristics are shown • Uncertain P peak • R-R interval is inconsistent An SB example • Number heart beat is 40-60 / minute
- 8. Introduction – Selected Diseases • 2 diseases AVNRT & AVRT omitted from evaluation since # of patients are very low • SA and SAAWR evaluation combined as SA/SAAWR, since # of SAAWR patients are very low Atrioventricular Node Reentrant Tachycardia Atrial Fibrillation Atrial Tachycardia Atrioventricular Reentrant Tachycardia Sinus Tachycardia Atrial Flutter Supraventricular Tachycardia Sinus Bradycardia Sinus Atrium Sinus Atrium-Atriyal Rhytm
- 9. Method Patient Number Rep. ECG P1 P2 P3 P4 P5 P6 P7 Disease Patient #1 11 44.83 467.61 1.33 130.17 660.67 1 SR Patient #2 9 45.29 37.27 1.79 96.57 577.43 0 AFIB Patient #3 0.941 8 52.43 341.70 1.24 112.71 578.14 1 SB Patient #10646 0.941 26 30.29 1287.8 1.24 89.86 454.57 0 AF Unique Parameters Other Parameters • 7 parameters are calculated for each ECG and by using the given disease information, random forest algorithm is tested. A ruleset is created which predicts the correct disease with %98.7 accuracy. • A software is developed which predicts the heart disease by using this ruleset for a given ECG 1
- 10. Method- About Polynomial Simulation We used a specific method developed by ourselves, which enables to find a polynomial function which passes some certain points Let’s consider a 4th degree polynomial function 2 • Put x=1; x=2; x=3; x=4; x=5; x=6 for this function • Write this 6 equation to the base of triangle (left side figure) 3 • Calculate differences of two consequtive equations • Write the new equation to top row, and construct this triangle till reaching 0 4
- 11. Method- About Polynomial Simulation Let’s find the suitable 4th degree polynomial function which passes from the points (1,1) (2,8) (3,27) (4,64) (5,125) 5 Write this 5 number to the base of triangle ( y values) (right side figure) 6 • Calculate differences of two consequtive numbers • Write the new number to top row, and construct this triangle till reaching 0 7
- 12. Method- About Polynomial Simulation Combine these 2 triangles Use only left column values 24a= 0 60a+6b=6 50a+12b+2c=12 15a+7b+3c+d=7 a+b+c+d+e = 1 So a=0, b=1, c=0; d=0; e= 0 means the polynomial function is certainly passes from points which we request 8
- 13. Method- About Entropy Entropy Calculation: Entropy is a mathematical value which measures the irregularity and uncertainty in a system. For the coefficients of the each heartbeat, we calculated the entropy value by using the formula below. It is called Shannon Entropy formula. Normalized entropy is the value which 𝐻 𝑥 value is divided to log 𝑛 9
- 14. Method- Calculated Parameters Unique Parameters • Average QRS length • First polynomial- actual difference Polynomial passing from Q peak, mid of Q-R peaks, R peak, mid of R-S peak, S peak • Entropy value for coefficients of polynomials Poynomial passing from each peak • P wave direction calculated by polynomial simulation 2nd degree polynomial passing from P peak to detect the direction of P peak 10 • Average QRS length mainly helps to identify ST, SB, AFIB and SVT • First polynomial-actual difference mainly helps to identify SA • Entropy value mainly helps to identify SVT, SA • P wave direction mainly helps to identify AF, AFIB or AT
- 15. Method- Calculated Parameters Parameters used in previous studies • # of heartbeat • PR interval length • R-R interval length 11 • # of heart beat and R-R interval length mainly helps to identify SB, ST, SVT • PR interval length length mainly helps to identify AFIB
- 16. Method- About Random Forest Random Forest Algorithm uses decision trees. It combines several decision trees to have a more accurate model. The final decision tree is constructed by joining each decision tree estimation. 12
- 17. Method- Random Forest Why Random Forest? • After calculating all parameters for all ECG, 6 different ML algorithm is tested for AF disease by using Weka with default parameters. Random Forest gave best results, so it is applied for all diseases. (10-fold cross validation with %20 testing data) 13 Algorithm Name F1 Score Precision Recall Duration (sec) Random Forest 0.98 0.98 0.98 0.33 SVM 0.96 0.97 0.96 0.05 ZeroR 0.83 0.83 0.83 0.01 BayesNet 0.97 0.96 0.96 0.03 Logistic Regression 0.97 0.96 0.97 0.05 AdaBoost 0.95 0.94 0.95 0.08
- 18. Results- Comparison Our Algorithm Zheng & others (2020) F1 Score Precision Recall F1 Score Precision Recall Diseases AFIB SB SA/SAAWR SR AT ST SVT AF Weighted Avg. 0.979 0.979 0.979 0.948 0.950 0.947 0.996 0.996 0.996 0.881 0.882 0.881 0.985 0.984 0.985 0.989 0.990 0.989 0.993 0.993 0.993 0.991 0.988 0.995 0.987 0.986 0.987 0.941 0.938 0.944 0.993 0.99 0.996 0.977 0.972 0.982 0.949 0.953 0.944 0.97 0.971 0.97 Zheng&others(2020) made a similar study with same dataset. When we compare all diseases with their study, our result is better except SA. They combined SR and SA in their study since it is easy to distinguish. Our weighted average F1 score is 0.987 1
- 19. Results- Benefits of parameters EXCLUDED PARAMETER F! SCORE F! DIFF. PRECISION RECALL ALL PARAMETERS INCLUDED 0.979 0 0.979 0.979 # of HEART BEAT 0.952 0.025 0.952 0.952 AVERAGE QRS LENGTH 0.946 0.033 0.948 0.945 FIRST POLYNOMIAL-ACTUAL DIF. 0.966 0.013 0.967 0.965 ENTROPY VALUE 0.956 0.023 0.957 0.955 PR INTERVAL LENGTH 0.952 0.027 0.952 0.95 R-R INTERVAL LENGTH 0.952 0.027 0.952 0.95 P WAVE DIRECTION 0.935 0.044 0.937 0.934 ALL UNIQUE PARAMETERS 0.931 0.048 0.930 0.932 ALL OTHER PARAMETERS 0.95 0.029 0.95 0.95 • We removed each parameter, and check the F1 score for AF disease. So, biggest contribution is coming from P wave direction and our unique parameters seems very effective.
- 20. Results- User Interface Load ECG as a csv file 1 See ECG, you can zoom and see details 2 See each heart beat and peaks (also you can zoom in-out) 3 Check polynomial function passing from each heartbeat 4
- 21. Results- User Interface Press this buton, to see prediction 5 You can see this window (the possible disease, and proposals 6
- 22. Results- Codes All codes are coded with phyton. The libraries used in this Project are listed below. 2 Nump Neurokit2 Math Os Csv Sklearn Pyqt Matplotlib.pyplot pandas All decision trees for each disease combined and coded to construct a single decision tree (to predict single disease for each ECG) 3
- 23. Discussion- Future Work • Our software will help users to interpret ECG more accurately and correctly. • In order to improve SA performance, a new parameter can be studied • Only 1 channel information is used in our study, 12 channel data can be used for better analysis • Our polynomial simulation method can be used not only for rhythm related diseases prediction, but also for other heart diseases like heart attack estimation. 1
- 24. References 1) McNamara K., Alzubaidi H., Jackson J.K. Cardiovascular disease as a leading cause of death: how are pharmacists getting involved? 2) Integr. Pharm. Res. Pract., 9 (2021), sf. 1-12 3) Gaidai O., Cao Y., Loginov S. Global cardiovascular diseases death rate prediction Curr. Problems Cardiol. (2023), Article 101622 4) World Health Organization. Cardiovascular diseases. World Health Organization. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 5) World Health Organization. Cardiovascular diseases. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/cancer 6) What is an electrocardiogram (ECG)? - informedhealth.org - NCBI bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK536878/ 7) Sinus bradycardia - statpearls - NCBI bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK493201 8) Centers for Disease Control and Prevention. (2022, October 14). Atrial fibrillation. Centers for Disease Control and Prevention. https://www.cdc.gov/heartdisease/atrial_fibrillation.htm 9)PREETAM, T. V. N. (2020) ECG SIGNAL ANALYSIS AND PREDICTION OF HEART ATTACK WITH THEHELP OF OPTIMIZED NEURAL NETWORK. 10) Zheng, J., Zhang, J., Danioko, S., Yao, H., Guo, H., & Rakovski, C. (2020). A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Scientific Data, 7(1). doi:10.1038/s41597-020-0386-x 11) Hangyuan, G. (2019, November 29). A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. figshare. https://figshare.com/collections/ChapmanECG/4560497 12) Şahin, M. (2018). ÇOKGENSEL SAYILARLA 3 BOYUTLU TOPLAMSAL YAPILAR, GENELLEŞTİRİLMELERİ VE ÖZELLİKLERİ. 2018 Tübitak 2204-A lise öğrencileri araştırma projeleri yarışması. 13) Gray, R. M. (2011), Entropy and Information Theory, Springer. sf. 61-65 14) Breiman, L. Random Forests. Machine Learning 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
- 25. Acknowledgement Thanks to our advisor Kemal Çelik and our families for their great support